Strategies for formulating and solving two-stage problems for process design under uncertainty

This paper describes a systematic procedure for formulating process design under uncertainty as two-stage problems and proposes a computational scheme for solving such problems. This formulation procedure introduces extra variables and constraints arising from capacity requirements and fixed computational order and generates all possible allocations of variables and constraints. The proposed computational scheme combines experimental design with gradient-based NLP algorithms. With the Han-Powell algorithm adapted for this purpose, this scheme proves to be more efficient than the method by Malik and Hughes1].